Temporal dynamics of socioeconomic inequalities in depressive and anxiety symptoms during the COVID-19 pandemic: a scoping review

Background The existence of socioeconomic inequalities in the prevalence of symptoms of depression and anxiety is widely acknowledged, and individuals from lower socioeconomic backgrounds tend to exhibit higher rates of symptoms. However, the direction in which the COVID-19 pandemic has influenced these disparities remains uncertain. We therefore aimed to systematically outline the available evidence on the temporal dynamics of socioeconomic inequalities in symptoms related to depression and anxiety during the COVID-19 pandemic across high-income countries. Methods A scoping review was conducted by searching the databases Embase, Scopus and PsycINFO. According to pre-defined eligibility criteria, two reviewers independently screened titles and abstracts as well as full texts of the compiled records. Data from the included studies were extracted using a standardised data-extraction form and analysed numerically and narratively. The scoping review followed the PRISMA-ScR guidelines. Results A total of 49 studies comprising 149 analyses of socioeconomic indicators in relation to symptoms of depression and anxiety were included. Despite heterogeneous study designs and results, there was a tendency of increasing (40.9%; n = 61) or persistent (38.2%; n = 57) inequality trends to the detriment of those in socially more disadvantaged positions. Increasing inequalities were most pronounced when income was used as a socioeconomic indicator. Groups with lower socioeconomic status appeared most vulnerable in the initial phase of the COVID-19 pandemic. Throughout the pandemic, dynamics were diverse, with persistent trends most frequently reported. Conclusion Overall, to the detriment of those with lower socioeconomic status, mental-health inequalities persisted or increased in most analyses. Continually monitoring socioeconomic inequalities over time is crucial, since this makes it possible to adapt prevention and intervention strategies to specific pandemic phases. Interventions targeting job security, income security and educational attainment could reduce mental-health inequalities. The results can contribute to preparedness plans for future pandemics and crises.


Introduction
Socioeconomic inequalities in the prevalence of common mental-health disorders, such as depression and anxiety disorders, are well known, with individuals from more disadvantaged socioeconomic backgrounds having higher prevalence of symptoms (1,2).Individuals suffering from material deprivation, unemployment, limited access to education and social isolation are particularly vulnerable to symptoms of depression and anxiety (3).
On March 11, 2020, the World Health Organization declared COVID-19 a pandemic (4).As one of the consequences, the COVID-19 Mental Disorders Collaborators predicted a global rise of 27.6% in cases of major depressive disorder and a 25.6% increase in anxiety disorder cases in 2020 (5).Reviews showed that symptoms of common mental disorders increased during the first period of the COVID-19 pandemic (6)(7)(8).A decline in symptoms was observed in the course of the pandemic, but figures did not return to pre-pandemic levels (7).
The increase in common mental-health disorders (5) was attributed to many changes in daily routines and the associated stress.Many people experienced resource depletion when being furloughed, dismissed or forced to work under new conditions (9,10).In addition, policy measures were implemented to curb the spread of the virus, encompassing actions such as the closure of childcare facilities and educational institutions, disrupting routine family dynamics (11).In addition, face-to-face psychosocial services were severely compromised (12).It is plausible that the compounding stressors arising from the COVID-19 pandemic amplified pre-existing mental-health challenges (13).Investigations have indicated a notable global escalation in the occurrence of psychopathologies associated with common mental disorders since the onset of the pandemic (5,12,14,15).
The stressors created by the COVID-19 pandemic may have affected certain socioeconomic groups more severely and may also have had different effects depending on socioeconomic status (SES) (16).As with health inequalities in general, socioeconomic inequalities in symptoms of depression and anxiety disorders may change or reproduce over time, leading to specific social-epidemiological patterns of disease distribution at different stages of the pandemic (17).
In the context of the possibly differential increase in symptoms of mental disorders depending on individuals' socioeconomic status during the COVID-19 pandemic, the following two theories may serve as theoretical background.The conservation of resources (COR) theory (18) posits that individuals strive to amass and safeguard resources to shield themselves from adversity and manage the requisites of daily life.Resources include valuable circumstances or contexts, personal attributes like self-efficacy, and tangible assets like financial means.A fundamental tenet of this theory underscores that the loss of -or perceived threat to -resources can impact on wellbeing (19).Despite its apparent paradox, the COR theory acknowledges that individuals with more resources might encounter diminished well-being in specific contexts.The extent of well-being impairment hinges upon how much their personal resources contract within that context (20).In the context of the COVID-19 pandemic, individuals with a higher SES might have experienced a greater detrimental impact on mental health due to greater resource depletion (11).
The second theory is the vulnerability-stress model (21).This model delineates the interactions between vulnerability and stress.The central premise is that both elements are prerequisites for the onset of a mental disorder (21).Vulnerability factors encompass (neuro) biological, psychological and environmental elements of vulnerability (22), contributing to an individual's susceptibility to mental disorders.The COVID-19 pandemic and its containment measures can be construed as stressors.Assuming heightened vulnerability among individuals with more disadvantaged SES (23), e.g., due to limited financial resources etc., experiences during the COVID-19 pandemic might have more frequently led to symptoms of common mental disorders (24).
First results from studies into changing socioeconomic inequalities in common mental disorders are inconclusive and sometimes contradictory.Some studies from the early stages of the COVID-19 pandemic suggested that socially disadvantaged groups were disproportionately affected by the COVID-19 pandemic and were at increased risk of developing common mental symptoms (13,25,26).However, contrasting findings from the USA suggest, for instance, that individuals with a higher education in the USA experienced a significant surge in depressive symptoms during the COVID-19 pandemic (11).Conversely, some studies indicate no differentiation in escalating mental-health problems and depressive symptoms across various SES groups (16, 27).However, comparisons were made between different periods, with some studies comparing to pre-pandemic levels (11, 16, 27) and some exclusively using peripandemic data (28)(29)(30).Understanding the time-dependent patterns of socioeconomic inequalities in mental-health complaints during the COVID-19 pandemic could help identify high-risk groups at different stages of the pandemic and enable timely public-health interventions to reduce mental-health inequalities and the overall burden of disease.
We therefore conducted a systematic scoping review to address the following research question: what is the evidence relating to trends in socioeconomic inequalities in symptoms of common mental-health disorders in high-income countries during the COVID-19 pandemic?To the best of our knowledge, this is the first systematic review of the evidence on SES-specific changes in symptoms of common mental disorders during the COVID-19 pandemic.

Methods
We conducted a systematic scoping review by searching the databases Scopus, Embase and PsycINFO in order to identify, collate, map and finally synthesise the available evidence (31,32).Scopus offered a comprehensive compilation of international medical and social scientific publications relevant to the research question (33).Embase covered biomedical journals and included Medline records from 2010 onwards (34), while PsycINFO complemented the database search with psychology-related publications (35).A detailed study protocol was published on the Open Science Framework (36) prior to the start of the study.This review follows the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) guidelines (37).

Eligibility criteria
The review only considered literature meeting predefined eligibility criteria (Table 1).
The selected studies were required to examine temporal dynamics in symptoms of common mental disorders during the COVID-19 pandemic, i.e., measures against depression or anxiety associated with socioeconomic factors had to be assessed at least at two different time points.Depressive and anxiety disorders are known to be frequently underdiagnosed in healthcare settings, and diverse access barriers might lead to an undertreatment (41).We therefore exclusively included studies that systematically ascertained symptoms via primary data collection in the general population and studies that used only secondary data; e.g.settlement data from health insurances were excluded to reduce selection bias.The initial time points may have preceded the onset of the COVID-19 pandemic, while the subsequent time points can have been during the pandemic.Alternatively, data collection might have taken place at two time points during the COVID-19 pandemic.Hence, cohort studies and repeated crosssectional studies were eligible for inclusion.In scoping reviews there is typically no assessment of the quality of evidence conducted due to the heterogeneity of the included studies (31,32).Peer-reviewed studies are generally subject to a minimum standard level of quality control.In contrast, non-peer reviewed publications, such as study preprints, do not have such requirement and hence were excluded.The target population encompassed the general population at a national or at least regional level.Studies with relatively homogeneous study populations (e.g., specific occupational cohorts) were excluded, as they did not permit systematic comparisons of risks between socioeconomic groups and might be particularly prone to collider bias.

Study selection and descriptive statistical analysis
Two reviewers (KH and LW) independently screened the titles and abstracts of the records obtained from the database search and the full texts of the identified studies.The software Rayyan (42) supported the title and abstract screening.To assess interrater reliability, Cohen's Kappa coefficient was calculated for both stages of study selection (43).In cases of disagreement, the records were discussed until a consensus was reached.A PRISMA flow chart summarised and visually represented the selection process.Microsoft Excel was used for all descriptive statistical analyses and the calculation of the Cohen's Kappa coefficient.

Data charting process
A standardised data-extracting chart was designed in an iterative process.After agreement on the data chart was reached, the following information was extracted from each included full-text article: author (year), country, number of participants, study design, observation period, outcome, socioeconomic indicator, outcome measures (e.g., odds ratio), most-affected SES-group, inequality trend, absolute or relative outcome measure.The results of temporal dynamics in socioeconomic inequalities were categorised into four groups: (1) persistent, (2) increasing, (3) decreasing or (4) crossing over time.For example, increasing inequalities were assumed if the prevalence difference between the highest and lowest socioeconomic category increased over time.Socioeconomic indicators were categorised based on income, education, occupation (including employment) or indexbased socioeconomic measures.In the following, the term study refers to the published study, including all analyses, and the term analysis refers to the one particular analysis that was reported by the study.

Evidence synthesis
First, all the articles were summarised in a table based on these previously defined components and adjusted in an iterative process.This table provided an overview of the temporal patterns observed for each outcome and socioeconomic indicator.Second, the principal descriptive results were summarised graphically or numerically.Finally, these preliminary descriptive results were used to narratively synthesise the evidence (44).

Results
Out of all 8,664 database records identified by our search, 49 articles met all the eligibility criteria comprising a total of 149 analyses that combined different socioeconomic indicators with symptoms of depression and anxiety over time.The study selection process is presented in detail in Figure 1.
The Cohen's Kappa coefficient in the title and abstract screening was 0.931.The interrater percent agreement was 99.8%.In the full-text screening, Cohen's Kappa coefficient was 0.799.The interrater percent agreement was 90.0%.
An overview of the included studies and their main findings is presented in Table 2.
Most of the included studies came from the USA (n = 14).5 studies were conducted in the UK and 4 were from the Netherlands and Hong Kong.Three studies were conducted in Germany and Korea, respectively.One study was conducted in each of the following countries: Norway, Italy, Romania, Luxembourg, Switzerland, Portugal, Canada, Chile, Qatar, Israel and Japan.In addition, there were 2 cross-continental studies and 3 studies relating to multiple European countries.
Of the included studies, 32 were cohort studies and 17 were repeated cross-sectional studies.Six studies were published in 2020, 14 in 2021, 23 in 2022, and 6 in 2023.
The size of the sample populations varied substantially with a mean number of participants of 143,222 and a median number of participants of 7,160.

Socioeconomic data
A comprehensive overview of the socioeconomic indicators used in the included studies can be found in Supplementary material S3.Income (n = 54) and education (n = 52) were the most frequently used socioeconomic indicators.Occupation, or employment, was used in 41 analyses.Two analyses assessed the SES by using the Index of Multiple Deprivation (IMD).Socioeconomic inequalities were most frequently assessed via relative measures (n = 90, 60.4%).In 22 (7.4%) analyses, only absolute measures and in 37 (24.8%)analyses, both relative and absolute measures were presented.

Outcome measures
The scores used to measure symptoms of depression and anxiety disorders are shown in Supplementary material S4.Among the included studies, depression was the most frequently investigated outcome (n = 38).24 studies investigated anxiety as an outcome.No study exclusively examined anxiety.In 24 studies, both depressive symptoms and symptoms of anxiety were assessed independently, and 11 studies measured a combined outcome of depression and anxiety.
The instrument most frequently used to measure symptoms of depression was the "Patient Health Questionnaire (PHQ)-9" (n = 16), and the second most used was the PHQ-2 (n = 8), a short version of the PHQ-9 (88).The "Depression Anxiety Stress Scale (DASS)-21 depression subscale" (n = 3) was used third most frequently.
If symptoms of depression and anxiety were measured in combination, the most frequently used instrument was the PHQ-4 (n = 6).All other instruments were used only once.
For the PHQ-9 and GAD-7 instruments, cut-off scores greater than or equal to 10 were used, or the cut-off score was not reported.For the short forms of these instruments, the PHQ-2 and GAD-2 questionnaires, cut-off scores ≥3 were used or not reported.The cut-off score for the PHQ-4 was generally ≥6, except for two studies, which set the cut-off score to 3 (78) or compared the average score over time (47).

Observation periods
Most studies (n = 31) used at least two comparison time points during the COVID-19 pandemic, while 15 studies compared at least one time point before the pandemic with at least one time point during the pandemic.In three studies, multiple time points were used for

Category Inclusion Exclusion
Study design • Cohort studies with at least two measurements

Population
• General population at the regional or national level • Representative of the national or regional population • Adults • Subgroups of population with certain conditions (e.g. outpatient clinic patients) • The population is not studied at the regional or national level but at the level of a city or a neighbourhood

Temporal dynamics of socioeconomic inequalities in symptoms of depression and anxiety
In almost all analyses, socially disadvantaged groups were most affected at baseline.Most often, the lowest category was the most affected, rarely the second-lowest category, and occasionally also the middle category of the respective component of SES.However, there were some exceptions.Three studies from Hong Kong (59,65,81), two cohort studies from Chile and the UK (49,71) and two studies across several European countries (56, 57) showed heterogenous results regarding the most affected socioeconomic group at baseline.Frontiers in Public 61) indicated increasing and 38.2% (n = 57) showed persistent inequalities.17.4% (n = 26) found decreasing inequalities over time (Figure 2).Among the five analyses showing crossover dynamics in inequalities, four demonstrated a transition from initially higher prevalences in more deprived populations to higher prevalences in more affluent populations over time.One analysis showed crossover dynamics to the detriment of more deprived parts of the population.Considering the initial situation, most of the analyses (63.8%, n = 95) showed either persistent or increasing inequalities, with socioeconomically disadvantaged populations being the most affected.
Increasing and persistent inequalities combined accounted for more than three-quarters of the findings (76.8%).Specifically, with regard to income, the majority of the analyses (n = 11) indicated an identified trend of increasing or persistent inequalities the early stages of the pandemic (March or April 2020) that negatively affected low SES groups (51,(82)(83)(84)(85)(86) in our more detailed analyses studies.
Upon closer examination of single countries, the USA stands out due to the highest number of included studies and contradictory results.Using the PHQ-9, Ettman et al. (51) showed that depressive symptoms tripled from 2017 to April 2020 in the USA, and low income and little savings were a risk for depressive symptoms in the initial stage of the COVID-19 pandemic.The population-representative survey study showed a stronger increase in depressive symptoms in the lowest and second-lowest income categories (51).In contrast, Wanberg et al. (11) came to different results, reporting that people with higher education had a higher risk of developing depressive symptoms in the USA between April 2019 and April 2020.The two studies differed in their pre-pandemic comparison date, screening tools, and methodology.
Regarding symptoms of common mental disorders and the dimension of educational attainment, a study conducted by Zhao et al. (86) in Hong Kong showed results similar to those of Ettman et al. in the USA.Specifically, Zhao et al. (86) analysed data on depression and anxiety symptoms from 2016 to April 2020 and found that individuals with low levels of formal education were particularly vulnerable to developing symptoms in the early stages of the pandemic.Mauz et al. (69) examined temporal trends in depressive symptoms based on attainment in Germany.Over the entire observation period from spring/summer 2019 to 2022, they observed an increase in the standardised proportions of individuals with a positive screen (69).A social gradient to the detriment of those with the lowest educational attainment was apparent throughout the whole observation Frontiers in Public group, there was a 5.2% increase (from 5.5%), while in the middle group, there was a 4.6% increase (from 10.6%), and in the low-level education group a 4.8% increase (from 16.2%) (69).Although all education groups had higher values at the end, the progression during the pandemic differed (69).The middle education group benefited at the beginning, and the higher education group was more affected as the pandemic progressed (69).
In the dimension of income or financial situation and inequality in symptoms of common mental-health disorders, a cohort study two different population cohorts from the UK ending in May 2020 found that socioeconomically disadvantaged people had higher levels of anxiety and depression, even when pre-pandemic levels were taken into account (64).Lower income, financial problems and deprivation were associated with a greater risk of symptoms of depression and anxiety.Anxiety symptoms exhibited a more profound increase compared to depressive symptoms.This surge was attributed to the uncertainty and sudden disruptions in daily life, coupled with health-related apprehensions (64).
Regarding symptoms of common mental disorders and the dimension of educational attainment, there were contrasting results in the USA, highlighted in the following section.Educational inequalities in depressive symptoms increased in the USA in May 2020 and then stabilised, according to Coley and Baum (46).In the case of symptoms of anxiety disorders, on the other hand, after an increase in May 2020, there was a decrease in educational inequality in autumn 2020 (46).Twenge et al. (82) reported that the relative increase in anxiety and depression in the USA was greater among those with higher education and income.However, those with lower education and income still had a higher prevalence (82).By contrast, Vahratian et al. (29) reported that the percentage of adults who had symptoms of anxiety disorder and depression increased during the pandemic, as did unmet mental-health needs.The most significant increases were among those aged between 18 and 29 and those with less than a high-school education (29).
In the dimension of income or financial situation and inequality in symptoms of common mental-health disorders, most studies concluded that inequalities have worsened or remained unchanged.In the UK, Saunders et al. (30) reported that from March to July 2020, individuals with lower incomes had higher symptoms at baseline, which then increased steadily during and after the relaxation of restrictions.Between May 2020 and February/March 2021,  53) demonstrated with a prospective longitudinal observational study that inequalities in the UK tended to converge over 20 weeks after the lockdown was announced but persisted until the end of the study period.36,520 participants had at least three measurements at different time points, and their data were analysed using latent growth models.Numerous risk factors associated with worse mental-health outcomes, such as female gender, younger age and lower educational attainment, were prominently observable during the early stages of the lockdown (53).As mental health gradually improved, the inequalities between these vulnerable demographic groups and those without these risk factors diminished.
However, the inequalities in mental health persisted, and vulnerable groups have remained at risk (53).

Discussion
This is the first systematic scoping review of the international evidence on the temporal dynamics of socioeconomic inequalities in symptoms of depression and anxiety disorders during the COVID-19 pandemic.Trends became evident despite the marked heterogeneity of the studies in terms of observation periods, outcomes, socioeconomic indicators and methods.Overall, most analyses showed increasing or persistent inequality trends.In the narrative synthesis of the studies, we identified a trend of increasing inequalities during the early stage of the pandemic that negatively affected low SES groups (46,50,51,64).During the pandemic, there were very heterogeneous developments, with persistent inequalities reported most frequently (46,69,71).Large inequalities were observed concerning income, including financial situation using different indicators of household economic situation (16,30,53,64,71,75,80).
The included studies discussed potential explanations for the observed trends.In the initial phase of the COVID-19 pandemic, socioeconomic inequality in symptoms of depression and anxiety in many analyses.One reason proposed was that segments of the population with lower SES were disproportionately affected, experiencing job losses, reduced working hours, having to continue to  As the COVID-19 pandemic progressed, there were reports of increased depressive or anxiety symptoms among population groups with higher SES.One cited reason was the prevalence of remote work conditions (11).Min et al. (70) suggested that differential access to information may have contributed to the fact that people with higher SES were increasingly affected by the pandemic.Previous studies reported that people were exposed to anxiety-provoking information via social media, and more frequent exposure to such information may have led to higher to mental-health deterioration when the future was unpredictable (91).In this regard, young, middle-aged, highly educated and high-income individuals were more likely to be exposed to negative information from the internet and social media than the general population (70).In addition, in several studies, the COR theory (18) was invoked to rationalise the fact that individuals with higher SES experienced relatively greater losses (11, 81).Higher SES could have been associated with a greater loss of interpersonal resources, as individuals with higher SES were likelier to work at home during the pandemic than those with lower SES (11).
Even in high-income countries, substantial heterogeneity existed in initial conditions, outcomes and mitigation measures during the COVID-19 pandemic.Some authors have suggested that this variability could account for divergent findings and conclusions, emphasising the importance of caution when extrapolating results across nations (13,77,83).Maffly-Kipp et al. (13) concluded that the severity of the COVID-19 pandemic influenced the relationship between socioeconomic factors and mental-health outcomes.In addition, van der Velden et al. (83) mentioned that differences in unemployment rates in March and April 2020 differed drastically between the USA and the Netherlands and might explain differences in results.A study from Luxembourg mentioned that Luxembourg implemented many policy measures to mitigate the spread of the disease and its economic consequences (77).Therefore, the authors emphasised that the mental-health impact should not be underestimated in other less affluent countries that were more seriously affected (77).A rapid review from 2021 in Germany showed that mental health was stable or advantageous for low SES groups during the first phase of the pandemic (92).The pronounced heterogeneity between countries could explain the discrepancy between the rapid review (92) and this scoping review.Some studies identified demographic subgroups at higher risk for mental-health problems during the COVID-19 pandemic.Concerning generational inequality, young adults were frequently mentioned as disproportionally vulnerable during the COVID-19 pandemic (29,30,50,54,55,62,66,70,71,75,77,78,87,91,93).This could be related to SES, as young people often have yet to reach a higher level of education, are still studying or in training, and consequently have a lower income (94).They are pursuing their education instead of working in a full-time paid job (94).For this age group, the literature discusses the greater overall disruption of life during the pandemic (57) against the background of the particularly great importance of social contacts with peers when leaving home.If we also take a look at family health, which is still important for young adults, a scoping review of mediators of health inequalities in children and adolescents pointed to the role of parental mental health, parenting practices, and the parent-child relationship, which could also serve as targets for interventions (95).The majority of studies suggested future pandemicpreparedness measures, including health-care access and targeted interventions for disadvantaged populations (53, 64,71,74,76).Identifying at-risk populations and collecting data on subgroups enables targeted support, as highlighted by multiple studies (30, 66,69,86).
Both relative and absolute measures of inequality were presented in only 24.8% of the analyses from the included studies.Research should precisely assess the magnitude and changes in socioeconomic health inequalities by consistently employing summary metrics for absolute and relative inequalities (96).This approach helps prevent biased interpretations of health-inequality trends resulting from the selective reporting of inequality measures (96).In future cohort or cross-sectional study publications, it would be desirable to present relative and absolute measures to view the development in its entirety.The measurement instruments of depressive symptoms and symptoms of anxiety disorders and their corresponding cut-off values were uniform using the PHQ-9 and GAD-7 and their short forms.By contrast, the study designs and methodology were very heterogeneous.Standardised study designs and methodology would be desirable to answer social-epidemiological questions.Subsequent investigations are imperative to quantitatively elucidate the extent of the correlation between socioeconomic indicators and symptoms of common mental disorders over time.Addressing this inquiry will forthcoming reviews to concentrate on narrower sets of socioeconomic indicators (e.g., household income categories), outcomes (e.g., PHQ-9, GAD-7) and periods (e.g., specific waves during the pandemic).Continuous, unified monitoring and comprehension of developments might help to develop more targeted support.

Strengths and limitations
This is the first systematically conducted scoping review the temporal dynamics of socioeconomic inequalities related to symptoms of depression and anxiety disorders during the in high-income countries.The systematic literature search contributed to achieving a high degree of comprehensiveness and ensured the replicability of the results.However, it could not be entirely ruled out that additional relevant records were perhaps obtained via supplementary databases or manual searches.Systematic assessment of methodological limitations of the included studies was not possible due to the heterogeneity of the studies, which might have introduced bias into the narrative synthesis (97).Nevertheless, this approach of a scoping review appeared most fitting considering the review's objectives of compiling a first and comprehensive overview.Potential selection and publication bias are concerns due to the review's exclusive focus on peer-reviewed publications.Studies that found null results were possibly neglected and not published.This bias could have been mitigated by incorporating non-peer-reviewed literature.However, since a structured assessment of evidence quality could not be executed (32), the eligibility criteria were confined to peer-reviewed articles to enhance the quality of the findings encompassed in the study.For reasons of comparability, this review was limited to high-income countries (38,39), which should be borne in mind when interpreting the results.In addition, this study included only articles published in English or German.This approach can limit the generalisability of the results.

Conclusion
This scoping review can contribute to improving preparedness for future pandemics or crises.It became apparent that social inequality in mental health increased or at least persisted in most cases to the disadvantage of the deprived population.Considering the temporal dynamics of mental-health inequalities can help prevent harmful effects on the mental health of specific population groups during different pandemic stages.Job security, income security, educational attainment and community-based initiatives, including targeted prevention and intervention programmes that consider diverse socioeconomic backgrounds, could be important targets for interventions to address health inequalities.Socioeconomic inequalities must be monitored to adapt prevention and intervention measures to specific phases.Reducing socioeconomic inequalities is crucial to improving population health and achieving equity in the face of a significant burden of common mental-health disorders and existing inequalities.
Population studied: children and adolescents Socioeconomic status • Indices of individual-level socioeconomic status • Income • Education • Occupation, incl.Employment • Area-level socioeconomic indices • Studies with an exclusive focus on race/ethnicity Outcome • Psychopathological symptoms of depression • Psychopathological symptoms of anxiety disorders • Psychopathological symptoms of depression and anxiety combined • Measures via any questionnaires (e.g.PHQ-9, PROMIS, Becks Depression-Inventory II) in any form (e.g., interview in person, interview by phone, written questionnaire) • Exclusive focus on suicide • Other outcomes (e.g., well-being, life satisfaction, psychopathological symptoms in general, COVID-19 related anxiety, and stress) Country • High-income countries (40) • Low−/lower-middle and upper-middle income countries comparison, so that analyses were possible both before-to-during and during the COVID-19 pandemic (54, 69, 82).The earliest start of the observation period compared from before to during the pandemic in the included studies was in 1999, while the latest start was in November 2019.The earliest onset for a comparison period within the pandemic was March 2020, while the latest beginning was June 2021.All studies' mean duration of the observation period was 736 days, with a median of 365 days and a range from 32 days to 20 years.Frequently, studies used data showing temporal dynamics based on monthly or yearly outcome rates.Multiple studies, however, compared several pandemic waves or specific time points associated with contextual factors, such as the introduction of containment orders.Additional details on the observation periods can be found in Supplementary material S5.

FIGURE 1 PRISMA
FIGURE 1PRISMA flow chart of the study selection process.

FIGURE 2
FIGURE 2 Number of analyses showing temporal dynamics of socioeconomic inequalities in depressive and anxiety symptoms in all included studies.

FIGURE 3
FIGURE 3Number of analyses showing temporal dynamics in depressive and anxiety symptoms from before the pandemic to during the pandemic.

FIGURE 4
FIGURE 4 Number of analyses showing temporal dynamics of socioeconomic inequalities in depressive and anxiety symptoms during the pandemic.
, low-and middle-income countries were excluded based on the World Bank's list published in 2021 (40).Database-specific search strings were developed for each concept (see Supplementary material S2), tailored to the respective databases, including relevant expanding terms like Emtree thesaurus for the Embase search.A scientific librarian was consulted during the search string development process.An additional filter was used to restrict the publication time from 2020 until the day of the search in 2023.Search terms were applied to search within titles and abstracts.The search in the electronic databases Scopus, Embase and PsycINFO was conducted on 14 May 2023.

TABLE 2
Summary table of the included studies and their main findings.